CEO Pandemic Analyses

Prep data

Load necessary packages and set Working Directory

if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse,zoo,lubridate,plotrix,ggpubr, caret, broom, kableExtra, reactable, effsize, install = T)
setwd("/Users/stevenmesquiti/Desktop/CEO Project/Manuscript analyses") 

Define aesthetics

colors =  c("CEO" = "dodgerblue3","Reddit" = "red")
palette_map = c("#3B9AB2", "#EBCC2A", "#F21A00")
palette_condition = c("#ee9b00", "#bb3e03", "#005f73")

plot_aes = theme_classic() +
  theme(legend.position = "top",
        legend.text = element_text(size = 10),)+
  theme(text = element_text(size = 16, family = "Futura Medium")) + 
  theme(axis.text.x=element_text(angle=45, hjust=1)) +
  theme(plot.title.position = 'plot', 
        plot.title = element_text(hjust = 0.5, face = "bold", size = 16)) + 
  theme(axis.text=element_text(size=16),
        axis.title=element_text(size=20,face="bold"))+
  theme(plot.title.position = 'plot', 
        plot.title = element_text(hjust = 0.5, face = "bold", size = 20)) +
  theme(axis.text=element_text(size = 14),
        axis.title=element_text(size = 20,face="bold"))

Write our Table Funcions

baseline_ttest <- function(ttest_list) {
  # Extract relevant information from each test and store in a data frame
   ttest_df <- data.frame(
    Group1 = seq(0, 0, 1),
    Group2 = seq(1, 24, 1),
    t = sapply(ttest_list, function(x) paste0("t(", round(x$parameter, 3), ") = ", round(x$statistic, 3))),
    p_value = sapply(ttest_list, function(x) x$p.value)
  )
  
  # Format p-values as scientific notation
  ttest_df$p_value <- format(ttest_df$p_value, scientific = T)
  
  # Rename columns
  colnames(ttest_df) <- c("t", "t + 1 ", "t-statistic", "p-value")
  
  # Create table using kableExtra
  kable(ttest_df, caption = "Summary of Welch's t-Tests", booktabs = TRUE) %>%
   kableExtra::kable_styling()
}

post_pandemic_summary <- function(ttest_list) {
  # Extract relevant information from each test and store in a data frame
  ttest_df <- data.frame(
    Group1 = seq(12,23,1),
    Group2 = seq(13,24,1),
    t = sapply(ttest_list, function(x) paste0("t(", round(x$parameter, 3), ") = ", round(x$statistic, 3))),
    p_value = sapply(ttest_list, function(x) x$p.value)
  )
  
  # Format p-values as scientific notation
  ttest_df$p_value <- format(ttest_df$p_value, scientific = T)
  
  # Rename columns
  colnames(ttest_df) <- c("t", "t + 1 ", "t-value", "p-value")
  
  # Create table using kableExtra
  kable(ttest_df, caption = "Summary of Welch's t-Tests", booktabs = TRUE) %>%
   kableExtra::kable_styling()
}



baseline_cohen_d <- function(cohen_d_list) {
  # Extract relevant information from each test and store in a data frame
  cohen_d_df <- data.frame(
    Group1 = seq(0,0,1),
    Group2 = seq(1,24,1),
    Cohen_d = sapply(cohen_d_list, function(x) x$estimate)
  )
  
  # Rename columns
  colnames(cohen_d_df) <- c("t", "t + 1", "Cohen's d")
  
  # Create table using kableExtra
  kable(cohen_d_df, caption = "Summary of Cohen's D", booktabs = TRUE) %>%
   kableExtra::kable_styling()
}

post_cohen_d <- function(cohen_d_list) {
  # Extract relevant information from each test and store in a data frame
  cohen_d_df <- data.frame(
    Group1 = seq(12,23,1),
    Group2 = seq(13,24,1),
    Cohen_d = sapply(cohen_d_list, function(x) x$estimate)
  )
  
  # Rename columns
  colnames(cohen_d_df) <- c("t", "t+1", "Cohen's d")
  
  # Create table using kableExtra
  kable(cohen_d_df, caption = "Summary of Cohen's D", booktabs = TRUE) %>%
   kableExtra::kable_styling()
}

baseline_mean_diff <- function(mean_diff_list) {
  # Extract relevant information from each mean difference calculation and store in a data frame
  mean_diff_df <- data.frame(
    Group1 = seq(0,0,1),
    Group2 = seq(1,24,1),
    mean_diff = mean_diff_list
  )
  
  # Rename columns
  colnames(mean_diff_df) <- c("t", "t+1", "Mean Difference")
  
  # Create table using kableExtra
  kable(mean_diff_df, caption = "Summary of Mean Differences", booktabs = TRUE) %>%
   kableExtra::kable_styling()
}


post_mean_diff <- function(mean_diff_list) {
  # Extract relevant information from each mean difference calculation and store in a data frame
  mean_diff_df <- data.frame(
    Group1 = seq(12,23,1),
    Group2 = seq(13,24,1),
    mean_diff = mean_diff_list
  )
  
  # Rename columns
  colnames(mean_diff_df) <- c("t", "t+1", "Mean Difference")
  
  # Create table using kableExtra
  kable(mean_diff_df, caption = "Summary of Mean Differences", booktabs = TRUE) %>%
   kableExtra::kable_styling()
}

Load in the CEO Data

data  <-  read_csv("https://raw.githubusercontent.com/scm1210/Language_Lab_Repro/main/Big_CEO.csv") #read in the data from github 

data <- data["2019-03-01"<= data$Date & data$Date <= "2021-04-01",] #subsetting covid dates 

nonclean <- nrow(data)

data <- data %>% filter(WC<=5400) %>% #filter out based on our exclusion criteria
  filter(WC>=25)

clean <- nrow(data)

nonclean - clean

## [1] 191

data$month_year <- format(as.Date(data$Date), "%Y-%m") #reformat 

data_tidy <- data %>% dplyr::select(Date, Speaker, Analytic, cogproc,allnone,we,i,emo_anx) %>%
  mutate(Date = lubridate::ymd(Date),
         time_month = as.numeric(Date - ymd("2019-03-01")) / 30, #centering at start of march
         time_month_quad = time_month * time_month) #making our quadratic term

data_tidy$Date_off <- floor(data_tidy$time_month) #rounding off dates to whole months using ceiling function (0 = 2019-03, 24 = 2021-04)
data_tidy$Date_covid <- as.factor(data_tidy$Date_off) #factorize

Load in Reddit data

reddit <- read_csv("/Users/stevenmesquiti/Dropbox/CEO-data/LIWC22-data/BLM_LIWC22_cleaned.csv")

reddit <- reddit %>%
  mutate(month_year = format(Date, "%Y-%m"))

reddit <- reddit %>% filter(WC<=5400)   %>% 
  filter(WC>=25)




reddit_tidy <- reddit %>% dplyr::select(Date, Analytic, cogproc,we,i) %>%
  mutate(Date = lubridate::ymd(Date),
         time_month = as.numeric(Date - ymd("2019-03-01")) / 30) #making our quadratic term


reddit_tidy$Date_off <- floor(reddit_tidy$time_month) #rounding off dates to whole months using ceiling function (0 = 2019-03, 24 = 2021-04)
reddit_tidy$Date_covid <- as.factor(reddit_tidy$Date_off) #factorize

Create Tidy Data for Graphs

df <- read_csv("https://raw.githubusercontent.com/scm1210/Language_Lab_Repro/main/Big_CEO.csv")#put code here to read in Big CEO data
df <- df %>% filter(WC<=5400)   %>% 
  filter(WC>=25)

df$month_year <- format(as.Date(df$Date), "%Y-%m") ###extracting month and year to build fiscal quarter graphs, need a new variable bc if not it'll give us issues

df2 <- df %>%#converting our dates to quarterly dates 
  group_by(month_year) %>% ###grouping by the Top100 tag and date 
  summarise_at(vars("Date","WC", "Analytic", "Clout", "Drives","cogproc","focuspast","focuspresent",
                    "focusfuture","power","allure","we","i",'insight','emo_anx','allnone'),  funs(mean, std.error),) #pulling the means and SEs for our variables of interest

df2 <- df2["2019-01"<= df2$month_year & df2$month_year <= "2021-03",] #covid dates 



reddit_tidy_2 <- reddit %>%#converting our dates to quarterly dates 
  group_by(month_year) %>% ###grouping by the Top100 tag and date 
  summarise_at(vars("Date","WC", "Analytic","cogproc","we","i"),  funs(mean, std.error),)

Write our Stats Functions

We were interested in how language changed relative to baseline one year pre-pandemic, as well as how language changed after the Pandemic.

As a result we ran two separate set of analyses comparing t(time zero) to t[i] and t(12 months after our centered data point) to t + 1. The groups you see will be centered on 03/2019. That is, 12 = 03/2020, 13 = 04/2020, etc. etc.

Analytic Thinking

analytic_my.t = function(fac1, fac2){
  t.test(data_tidy$Analytic[data_tidy$Date_covid==fac1], 
         data_tidy$Analytic[data_tidy$Date_covid==fac2])
} #writing our t-test function to compare t to t[i] 

analytic_my.d = function(fac1, fac2){
  cohen.d(data_tidy$Analytic[data_tidy$Date_covid==fac1], 
          data_tidy$Analytic[data_tidy$Date_covid==fac2])
} #function for cohen's d

analytic_mean <-  function(fac1, fac2){
  mean(data_tidy$Analytic[data_tidy$Date_covid==fac1])- 
    mean(data_tidy$Analytic[data_tidy$Date_covid==fac2])
} #function to do mean differences

Cognitive Processing

cogproc_my.t = function(fac1, fac2){
  t.test(data_tidy$cogproc[data_tidy$Date_covid==fac1], 
         data_tidy$cogproc[data_tidy$Date_covid==fac2])
} #writing our t-test function to compare t to t[i] 


cogproc_my.d = function(fac1, fac2){
  cohen.d(data_tidy$cogproc[data_tidy$Date_covid==fac1], 
          data_tidy$cogproc[data_tidy$Date_covid==fac2])
} #function for cohen's d

cogproc_mean <-  function(fac1, fac2){
  mean(data_tidy$cogproc[data_tidy$Date_covid==fac1])- 
    mean(data_tidy$cogproc[data_tidy$Date_covid==fac2])
} #function to do mean differences

I-words

i_my.t = function(fac1, fac2){
  t.test(data_tidy$i[data_tidy$Date_covid==fac1], 
         data_tidy$i[data_tidy$Date_covid==fac2])
} #writing our t-test function to compare t to t + 1 

i_my.d = function(fac1, fac2){
  cohen.d(data_tidy$i[data_tidy$Date_covid==fac1], 
          data_tidy$i[data_tidy$Date_covid==fac2])
} #function for cohen's d


i_mean <-  function(fac1, fac2){
  mean(data_tidy$i[data_tidy$Date_covid==fac1])- 
    mean(data_tidy$i[data_tidy$Date_covid==fac2])
} #function to do mean differences

We-words

we_my.t = function(fac1, fac2){
  t.test(data_tidy$we[data_tidy$Date_covid==fac1], 
         data_tidy$we[data_tidy$Date_covid==fac2])
} 

we_my.d = function(fac1, fac2){
  cohen.d(data_tidy$we[data_tidy$Date_covid==fac1], 
          data_tidy$we[data_tidy$Date_covid==fac2])
} #function for cohen's d

we_mean <-  function(fac1, fac2){
  mean(data_tidy$we[data_tidy$Date_covid==fac1])- 
    mean(data_tidy$we[data_tidy$Date_covid==fac2])
} #function to do mean differences

Tidy data

Data transformations

• None

Exclusions

• Excluded texts that were shorter than ** 25 words ** and greater than ** 5,400 words **!

Summary of the Data

Range of Dates

range(data$Date)

## [1] "2019-03-01" "2021-04-01"

Number of Speakers

speakers <- data %>%
  select(Speaker) %>%
  unique() %>%
  dplyr::summarize(n = n()) %>%
  reactable::reactable(striped = TRUE)
speakers

Number of Transcripts

transcripts <- data %>%
  select(1) %>%
  dplyr::summarize(n = n()) %>%
  reactable::reactable(striped = TRUE)
transcripts

Mean Word Count

word_count <- data %>%
  select(WC) %>%
  dplyr::summarize(mean = mean(WC)) %>%
  reactable::reactable(striped = TRUE)
word_count

How did language change after the Pandemic?

Analytic Thinking

T-test

analytic_ttest<- mapply(analytic_my.t,seq(12,23,1), seq(13,24,1),SIMPLIFY=F) #compare t (first parantheses) to t[i] (second parentheses)increasing by 1
post_pandemic_summary(analytic_ttest)

Summary of Welch’s t-Tests

t	t + 1	t-value	p-value
12	13	t(525.793) = 5.085	5.124345e-07
13	14	t(373.064) = -2.595	9.838752e-03
14	15	t(252.035) = -1.673	9.565479e-02
15	16	t(377.617) = 1.924	5.508471e-02
16	17	t(200.57) = -2.212	2.808412e-02
17	18	t(218.933) = -1.687	9.298455e-02
18	19	t(262.609) = 0.62	5.358364e-01
19	20	t(128.217) = 0.874	3.838664e-01
20	21	t(230.756) = -1.54	1.249802e-01
21	22	t(94.317) = 1.953	5.374259e-02
22	23	t(55.552) = -1.15	2.551600e-01
23	24	t(2141.372) = -1.718	8.595937e-02

Cohen’s D

analytic_d <- mapply(analytic_my.d,seq(12,23,1), seq(13,24,1),SIMPLIFY=FALSE) 
post_cohen_d(analytic_d)

Summary of Cohen’s D

t	t+1	Cohen’s d
12	13	0.3274589
13	14	-0.1597933
14	15	-0.1320224
15	16	0.1935631
16	17	-0.1616992
17	18	-0.1481301
18	19	0.0709701
19	20	0.0898748
20	21	-0.1246402
21	22	0.2681803
22	23	-0.1598304
23	24	-0.0739462

Mean Differences

analytic_meandiff <- mapply(analytic_mean, seq(12,23,1), seq(13,24,1)) #across all of the months comparing to time zero
post_mean_diff(analytic_meandiff)

Summary of Mean Differences

t	t+1	Mean Difference
12	13	4.734622
13	14	-2.190455
14	15	-1.844328
15	16	2.748318
16	17	-2.231753
17	18	-2.101267
18	19	1.158869
19	20	1.276462
20	21	-1.779122
21	22	4.065080
22	23	-2.075629
23	24	-0.994088

Cogproc

T-test

cogproc_ttest <-mapply(cogproc_my.t, seq(12,23,1), seq(13,24,1),SIMPLIFY=FALSE) #compare t (first parathese) to t[i] (second parantheses) increasing by 1
post_pandemic_summary(cogproc_ttest)

Summary of Welch’s t-Tests

t	t + 1	t-value	p-value
12	13	t(534.573) = -4.316	1.892660e-05
13	14	t(366.536) = 1.405	1.609866e-01
14	15	t(257.865) = 4.019	7.665356e-05
15	16	t(367.3) = -3.132	1.877275e-03
16	17	t(199.239) = 0.987	3.249415e-01
17	18	t(223.61) = 4.18	4.177506e-05
18	19	t(285.883) = -1.198	2.317513e-01
19	20	t(133.619) = -1.493	1.378047e-01
20	21	t(234.846) = 3.211	1.508000e-03
21	22	t(87.346) = -1.705	9.183489e-02
22	23	t(55.376) = 0.997	3.232089e-01
23	24	t(2145.127) = -0.999	3.177001e-01

Cohen’s D

cogproc_d <-mapply(cogproc_my.d, seq(12,23,1), seq(13,24,1),SIMPLIFY=FALSE)
post_cohen_d(cogproc_d)

Summary of Cohen’s D

t	t+1	Cohen’s d
12	13	-0.2755415
13	14	0.0887056
14	15	0.3007241
15	16	-0.3204553
16	17	0.0732556
17	18	0.3435609
18	19	-0.1329353
19	20	-0.1294167
20	21	0.2476709
21	22	-0.2453381
22	23	0.1405453
23	24	-0.0429758

Mean Differences

cogproc_meandiff <- mapply(cogproc_mean, seq(12,23,1), seq(13,24,1)) # comparing time zero [3/2019]across all of the months
post_mean_diff(cogproc_meandiff)

Summary of Mean Differences

t	t+1	Mean Difference
12	13	-0.6107287
13	14	0.1784774
14	15	0.6094504
15	16	-0.6540232
16	17	0.1559844
17	18	0.7442075
18	19	-0.2962170
19	20	-0.2746360
20	21	0.5304979
21	22	-0.5357971
22	23	0.2775877
23	24	-0.0886600

I-words

T-test

i_ttest <- mapply(i_my.t, seq(12,23,1), seq(13,24,1),SIMPLIFY=FALSE) #compare t (first paratheses) to t[i] (second parentheses) increasing by 1
post_pandemic_summary(i_ttest)

Summary of Welch’s t-Tests

t	t + 1	t-value	p-value
12	13	t(477.851) = -5.103	4.841738e-07
13	14	t(362.97) = 2.968	3.193717e-03
14	15	t(261.205) = 2.735	6.660709e-03
15	16	t(336.981) = -3.589	3.805206e-04
16	17	t(191.52) = 1.761	7.976208e-02
17	18	t(240.733) = 3.439	6.870032e-04
18	19	t(255.111) = -2.602	9.812584e-03
19	20	t(134.906) = 0.45	6.532009e-01
20	21	t(248.773) = 1.506	1.333518e-01
21	22	t(84.284) = 2.016	4.699962e-02
22	23	t(57.559) = -3.807	3.436805e-04
23	24	t(2135.84) = 4.409	1.087616e-05

Cohen’s D

i_d <- mapply(i_my.d,seq(12,23,1), seq(13,24,1),SIMPLIFY=FALSE)
post_cohen_d(i_d)

Summary of Cohen’s D

t	t+1	Cohen’s d
12	13	-0.3467518
13	14	0.1902125
14	15	0.1990807
15	16	-0.3757604
16	17	0.1451672
17	18	0.2369631
18	19	-0.3007221
19	20	0.0377993
20	21	0.1020099
21	22	0.2971566
22	23	-0.4621942
23	24	0.1900173

Mean Differences

i_meandiff <- mapply(i_mean,seq(12,23,1), seq(13,24,1)) # comparing time zero [3/2020]across all of the months
post_mean_diff(i_meandiff)

Summary of Mean Differences

t	t+1	Mean Difference
12	13	-0.2878044
13	14	0.1550533
14	15	0.1624754
15	16	-0.3241516
16	17	0.1289192
17	18	0.2083141
18	19	-0.2363725
19	20	0.0329017
20	21	0.0885966
21	22	0.2292627
22	23	-0.3911951
23	24	0.1657095

We-words

T-test

we_ttest <- mapply(we_my.t, seq(12,23,1), seq(13,24,1),SIMPLIFY=FALSE) #compare t (first parathese) to t[i] (second parantheses) increasing by 1
post_pandemic_summary(we_ttest)

Summary of Welch’s t-Tests

t	t + 1	t-value	p-value
12	13	t(527.076) = 4.104	4.708824e-05
13	14	t(378.819) = 0.912	3.625070e-01
14	15	t(253.139) = -3.323	1.023448e-03
15	16	t(373.961) = 2.465	1.416113e-02
16	17	t(197.518) = -0.338	7.360894e-01
17	18	t(229.495) = -4.276	2.793946e-05
18	19	t(262.602) = 2.551	1.130991e-02
19	20	t(131.794) = -0.142	8.871422e-01
20	21	t(238.212) = -1.94	5.361708e-02
21	22	t(84.062) = -0.295	7.685396e-01
22	23	t(55.764) = 0.856	3.958478e-01
23	24	t(2137.765) = -0.35	7.267188e-01

Cohen’s D

we_d <- mapply(we_my.d, seq(12,23,1), seq(13,24,1),SIMPLIFY=FALSE)
post_cohen_d(we_d)

Summary of Cohen’s D

t	t+1	Cohen’s d
12	13	0.2639367
13	14	0.0549934
14	15	-0.2594704
15	16	0.2501259
16	17	-0.0255875
17	18	-0.3276203
18	19	0.2920369
19	20	-0.0129636
20	21	-0.1443587
21	22	-0.0435999
22	23	0.1169953
23	24	-0.0150573

Mean Differences

we_meandiff <- mapply(we_mean, seq(12,23,1), seq(13,24,1)) # comparing time zero [3/2020]across all of the months
post_mean_diff(we_meandiff)

Summary of Mean Differences

t	t+1	Mean Difference
12	13	0.3777932
13	14	0.0763380
14	15	-0.3676046
15	16	0.3649285
16	17	-0.0365235
17	18	-0.4710551
18	19	0.4168557
19	20	-0.0182846
20	21	-0.2041654
21	22	-0.0608833
22	23	0.1582888
23	24	-0.0209555

How did language change relative to baseline (one year before the pandemic; 03/2019)?

Analytic Thining

T-test

analytic_ttest_baseline <-mapply(analytic_my.t,0, seq(1,24,1),SIMPLIFY=FALSE) #compare t (first parantheses) to t[i] (second parentheses)increasing by 1
baseline_ttest(analytic_ttest_baseline)

Summary of Welch’s t-Tests

t	t + 1	t-statistic	p-value
0	1	t(1161.463) = 1.503	1.332353e-01
0	2	t(1036.849) = 0.686	4.928577e-01
0	3	t(245.143) = 0.251	8.021842e-01
0	4	t(1120.104) = 2.673	7.630544e-03
0	5	t(1004.801) = 0.479	6.323643e-01
0	6	t(280.425) = 1.034	3.018785e-01
0	7	t(1049.944) = 2.667	7.759826e-03
0	8	t(993.351) = 1.405	1.604652e-01
0	9	t(328.093) = 1.015	3.109746e-01
0	10	t(286.24) = 1.551	1.221201e-01
0	11	t(1061.639) = 1.974	4.866575e-02
0	12	t(1272.101) = 1.305	1.919959e-01
0	13	t(623.937) = 5.777	1.200948e-08
0	14	t(929.477) = 5.152	3.153290e-07
0	15	t(370.165) = 1.422	1.558977e-01
0	16	t(316.924) = 3.926	1.060657e-04
0	17	t(918.086) = 3.257	1.166437e-03
0	18	t(302.234) = 0.117	9.068413e-01
0	19	t(164.423) = 0.846	3.986233e-01
0	20	t(920.439) = 3.736	1.981471e-04
0	21	t(331.793) = 0.639	5.230612e-01
0	22	t(63.201) = 2.617	1.108971e-02
0	23	t(1111.951) = 3.769	1.727388e-04
0	24	t(1125.188) = 2.433	1.514789e-02

Cohen’s D

analytic_D_baseline <- mapply(analytic_my.d,0, seq(1,24,1),SIMPLIFY=FALSE) 
baseline_cohen_d(analytic_D_baseline)

Summary of Cohen’s D

t	t + 1	Cohen’s d
0	1	0.0879752
0	2	0.0329980
0	3	0.0206107
0	4	0.1587215
0	5	0.0235235
0	6	0.0867045
0	7	0.1620807
0	8	0.0687147
0	9	0.0805849
0	10	0.1282654
0	11	0.1023933
0	12	0.0694416
0	13	0.3954264
0	14	0.2534133
0	15	0.1138341
0	16	0.3057368
0	17	0.1588173
0	18	0.0101558
0	19	0.0861013
0	20	0.1802980
0	21	0.0529819
0	22	0.3237240
0	23	0.2018620
0	24	0.1262979

Mean Differences

analytic_mean_baseline <- mapply(analytic_mean, 0, seq(1,24,1)) #across all of the months comparing to time zero
baseline_mean_diff(analytic_mean_baseline)

Summary of Mean Differences

t	t+1	Mean Difference
0	1	1.3114081
0	2	0.4935284
0	3	0.3039970
0	4	2.3251490
0	5	0.3411544
0	6	1.3027809
0	7	2.3954214
0	8	0.9976299
0	9	1.1986758
0	10	1.9188652
0	11	1.4369448
0	12	1.0438407
0	13	5.7784625
0	14	3.5880071
0	15	1.7436794
0	16	4.4919977
0	17	2.2602447
0	18	0.1589776
0	19	1.3178462
0	20	2.5943085
0	21	0.8151869
0	22	4.8802673
0	23	2.8046380
0	24	1.8105501

Cogproc

T-test

cogproc_ttest_baseline <- mapply(cogproc_my.t, 0, seq(1,24,1),SIMPLIFY=FALSE) #compare t (first parathese) to t[i] (second parantheses) increasing by 1
baseline_ttest(cogproc_ttest_baseline)

Summary of Welch’s t-Tests

t	t + 1	t-statistic	p-value
0	1	t(1156.51) = -0.51	6.103480e-01
0	2	t(1035.97) = -0.718	4.730063e-01
0	3	t(218.72) = -0.239	8.112280e-01
0	4	t(1119.697) = -1.842	6.578607e-02
0	5	t(1051.938) = -0.376	7.067326e-01
0	6	t(282.794) = 0.244	8.072301e-01
0	7	t(1029.213) = -1.714	8.679890e-02
0	8	t(1076.642) = -0.954	3.403915e-01
0	9	t(320.307) = 1.045	2.970093e-01
0	10	t(255.259) = -0.817	4.147599e-01
0	11	t(1147.575) = -0.725	4.688845e-01
0	12	t(1307.905) = -2.028	4.276280e-02
0	13	t(609.245) = -5.701	1.854777e-08
0	14	t(924.043) = -6.591	7.328808e-11
0	15	t(395.995) = -0.386	7.000311e-01
0	16	t(298.221) = -4.081	5.758392e-05
0	17	t(949.003) = -5.465	5.916345e-08
0	18	t(310.668) = 0.926	3.549182e-01
0	19	t(184.738) = -0.58	5.628182e-01
0	20	t(936.808) = -3.799	1.544264e-04
0	21	t(341.615) = 0.764	4.454529e-01
0	22	t(61.973) = -1.382	1.719203e-01
0	23	t(1140.023) = -1.069	2.852706e-01
0	24	t(1172.335) = -1.859	6.323237e-02

Cohen’s D

cogproc_D_baseline <- mapply(cogproc_my.d, 0, seq(1,24,1),SIMPLIFY=FALSE)
baseline_cohen_d(cogproc_D_baseline)

Summary of Cohen’s D

t	t + 1	Cohen’s d
0	1	-0.0298959
0	2	-0.0345459
0	3	-0.0213194
0	4	-0.1093919
0	5	-0.0180369
0	6	0.0203613
0	7	-0.1048291
0	8	-0.0445936
0	9	0.0841121
0	10	-0.0731906
0	11	-0.0364241
0	12	-0.1070381
0	13	-0.3938811
0	14	-0.3255788
0	15	-0.0297828
0	16	-0.3291694
0	17	-0.2601030
0	18	0.0788773
0	19	-0.0527050
0	20	-0.1809343
0	21	0.0622160
0	22	-0.1777619
0	23	-0.0568265
0	24	-0.0951265

Mean Differences

cogproc_mean_baseline <- mapply(cogproc_mean, 0, seq(1,24,1)) # comparing time zero [3/2020]across all of the months
baseline_mean_diff(cogproc_meandiff)

Summary of Mean Differences

t	t+1	Mean Difference
0	1	-0.6107287
0	2	0.1784774
0	3	0.6094504
0	4	-0.6540232
0	5	0.1559844
0	6	0.7442075
0	7	-0.2962170
0	8	-0.2746360
0	9	0.5304979
0	10	-0.5357971
0	11	0.2775877
0	12	-0.0886600
0	13	-0.6107287
0	14	0.1784774
0	15	0.6094504
0	16	-0.6540232
0	17	0.1559844
0	18	0.7442075
0	19	-0.2962170
0	20	-0.2746360
0	21	0.5304979
0	22	-0.5357971
0	23	0.2775877
0	24	-0.0886600

I-words

T-test

i_ttest_baseline <- mapply(i_my.t, 0, seq(1,24,1),SIMPLIFY=FALSE) #compare t (first paratheseses) to t[i] (second parentheses) increasing by 1
baseline_ttest(i_ttest_baseline)

Summary of Welch’s t-Tests

t	t + 1	t-statistic	p-value
0	1	t(1143.818) = -3.345	8.495412e-04
0	2	t(1155.183) = -1.196	2.318220e-01
0	3	t(213.553) = -0.191	8.486000e-01
0	4	t(1114.307) = -4.144	3.672274e-05
0	5	t(1056.559) = -0.648	5.173329e-01
0	6	t(278.03) = -1.611	1.082868e-01
0	7	t(1035.231) = -3.353	8.273950e-04
0	8	t(1066.958) = -2.058	3.981213e-02
0	9	t(265.192) = -1.417	1.577272e-01
0	10	t(284.305) = -2.775	5.890772e-03
0	11	t(1154.305) = -1.985	4.739397e-02
0	12	t(1263.498) = -0.332	7.399444e-01
0	13	t(571.485) = -5.028	6.644118e-07
0	14	t(958.88) = -3.709	2.197939e-04
0	15	t(390.578) = 0.221	8.248697e-01
0	16	t(253.435) = -3.925	1.115955e-04
0	17	t(1005.422) = -4.473	8.580050e-06
0	18	t(350.624) = 0.413	6.794966e-01
0	19	t(180.598) = -2.646	8.864330e-03
0	20	t(986.111) = -4.378	1.326045e-05
0	21	t(371.13) = -1.322	1.869275e-01
0	22	t(63.336) = 1.351	1.815790e-01
0	23	t(1250.838) = -5.622	2.322252e-08
0	24	t(1254.797) = -1.893	5.857980e-02

Cohen’s D

i_D_baseline <- mapply(i_my.d, 0, seq(1,24,1),SIMPLIFY=FALSE)
baseline_cohen_d(i_D_baseline)

Summary of Cohen’s D

t	t + 1	Cohen’s d
0	1	-0.1965974
0	2	-0.0543981
0	3	-0.0173720
0	4	-0.2467407
0	5	-0.0309676
0	6	-0.1358241
0	7	-0.2047181
0	8	-0.0966976
0	9	-0.1296303
0	10	-0.2305339
0	11	-0.0995545
0	12	-0.0176937
0	13	-0.3562055
0	14	-0.1785725
0	15	0.0172266
0	16	-0.3536629
0	17	-0.2047237
0	18	0.0327380
0	19	-0.2453415
0	20	-0.2010721
0	21	-0.1028381
0	22	0.1664219
0	23	-0.2903836
0	24	-0.0945412

Mean Differences

i_mean_baseline <- mapply(i_mean, 0, seq(1,24,1)) # comparing time zero [3/2020]across all of the months
baseline_mean_diff(i_mean_baseline)

Summary of Mean Differences

t	t+1	Mean Difference
0	1	-0.1747670
0	2	-0.0504304
0	3	-0.0148774
0	4	-0.2082233
0	5	-0.0265697
0	6	-0.1159251
0	7	-0.1744079
0	8	-0.0846426
0	9	-0.1162156
0	10	-0.1958046
0	11	-0.0842683
0	12	-0.0149918
0	13	-0.3027962
0	14	-0.1477429
0	15	0.0147325
0	16	-0.3094191
0	17	-0.1804999
0	18	0.0278142
0	19	-0.2085583
0	20	-0.1756567
0	21	-0.0870600
0	22	0.1422027
0	23	-0.2489924
0	24	-0.0832828

We-words

T-test

we_ttest_baseline <- mapply(we_my.t, 0, seq(1,24,1),SIMPLIFY=FALSE) #compare t (first parathese) to t[i] (second parantheses) increasing by 1
baseline_ttest(we_ttest_baseline)

Summary of Welch’s t-Tests

t	t + 1	t-statistic	p-value
0	1	t(1161.884) = 0.572	5.675785e-01
0	2	t(1008.446) = 1.592	1.117125e-01
0	3	t(214.746) = -1.069	2.864739e-01
0	4	t(1116.226) = 0.615	5.384335e-01
0	5	t(979.103) = 0.94	3.476349e-01
0	6	t(280.316) = -1.18	2.391716e-01
0	7	t(1067.876) = -0.204	8.386752e-01
0	8	t(972.543) = 0.65	5.160283e-01
0	9	t(351.29) = -0.631	5.286168e-01
0	10	t(309.043) = -0.968	3.339559e-01
0	11	t(1073.791) = -0.927	3.542624e-01
0	12	t(1197.173) = -0.4	6.891035e-01
0	13	t(676.589) = 3.36	8.220450e-04
0	14	t(890.336) = 5.66	2.040178e-08
0	15	t(395.823) = 0.423	6.723924e-01
0	16	t(317.82) = 3.39	7.875779e-04
0	17	t(889.197) = 5.136	3.456716e-07
0	18	t(361.984) = -0.716	4.741820e-01
0	19	t(191.377) = 2.309	2.199015e-02
0	20	t(873.543) = 4.18	3.205482e-05
0	21	t(390.061) = 0.867	3.866454e-01
0	22	t(64.772) = 0.229	8.197829e-01
0	23	t(1081.131) = 2.543	1.113820e-02
0	24	t(1080.954) = 2.287	2.237292e-02

Cohen’s D

we_D_baseline <- mapply(we_my.d, 0, seq(1,24,1),SIMPLIFY=FALSE)
baseline_cohen_d(we_D_baseline)

Summary of Cohen’s D

t	t + 1	Cohen’s d
0	1	0.0334412
0	2	0.0777773
0	3	-0.0966754
0	4	0.0362120
0	5	0.0468851
0	6	-0.0989057
0	7	-0.0122764
0	8	0.0321927
0	9	-0.0482579
0	10	-0.0764371
0	11	-0.0478523
0	12	-0.0216259
0	13	0.2228626
0	14	0.2873740
0	15	0.0326963
0	16	0.2635803
0	17	0.2566654
0	18	-0.0557482
0	19	0.2039772
0	20	0.2102911
0	21	0.0657068
0	22	0.0270689
0	23	0.1373736
0	24	0.1204946

Mean Differences

we_mean_baseline <- mapply(we_mean, 0, seq(1,24,1)) # comparing time zero [3/2020]across all of the months
baseline_mean_diff(we_mean_baseline)

Summary of Mean Differences

t	t+1	Mean Difference
0	1	0.0530735
0	2	0.1226640
0	3	-0.1575023
0	4	0.0544833
0	5	0.0717923
0	6	-0.1604908
0	7	-0.0190853
0	8	0.0495303
0	9	-0.0765531
0	10	-0.1217559
0	11	-0.0731274
0	12	-0.0334520
0	13	0.3443412
0	14	0.4206792
0	15	0.0530747
0	16	0.4180032
0	17	0.3814797
0	18	-0.0895754
0	19	0.3272803
0	20	0.3089956
0	21	0.1048303
0	22	0.0439469
0	23	0.2022358
0	24	0.1812803

2019-2021 Graphs

Analytic Thinking

Analytic <- 
  ggplot() +
  geom_line(data = df2, aes(x = Date_mean, y = Analytic_mean, color = "CEO"), #set our colors 
              linetype = 'solid') +
  geom_ribbon(data = df2, aes(x = Date_mean,ymin=Analytic_mean-Analytic_std.error, ymax=Analytic_mean+Analytic_std.error, fill = "CEO"), alpha=0.2) +
geom_line(data = reddit_tidy_2, aes(x = Date_mean, y = Analytic_mean, color = "Reddit"), #set our colors 
              linetype = 'solid') +
geom_ribbon(data = reddit_tidy_2, aes(x = Date_mean,ymin=Analytic_mean-Analytic_std.error, ymax=Analytic_mean+Analytic_std.error, fill = "Reddit"), alpha=0.2) +
  scale_x_date(date_breaks = "3 month", date_labels = "%Y-%m") +
  ggtitle("Analytic Thinking") +
  labs(x = "Month", y = 'Standardized score') +
  plot_aes + #here's our plot aes object
  geom_vline(xintercept = as.numeric(as.Date("2020-03-01")), linetype = 2) +
  geom_rect(data = df2, #summer surge
            aes(xmin = as.Date("2020-06-15", "%Y-%m-%d"), 
                xmax = as.Date("2020-07-20",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009) +
  geom_rect(data = df2, #winter surge
            aes(xmin = as.Date("2020-11-15", "%Y-%m-%d"), 
                xmax = as.Date("2021-01-01",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009)

Analytic <- Analytic + annotate(geom="text",x=as.Date("2020-07-01"),
                                y=45,label="Summer 2020 surge", size = 5) + 
  annotate(geom="text",x=as.Date("2020-12-03"),
           y=47,label="Winter 2020 surge", size = 5) 

Analytic <- Analytic +
  scale_color_manual(values = c(CEO = "dodgerblue3", Reddit = "red")) +
  scale_fill_manual(values = c(CEO = "dodgerblue3", Reddit = "red"))

Analytic

Analytic_CEO <- ggplot(data=df2, aes(x=Date_mean, y=Analytic_mean, group=1)) +
  geom_line(colour = "dodgerblue3") +
  scale_x_date(date_breaks = "3 month", date_labels = "%Y-%m") +
  geom_ribbon(aes(ymin=Analytic_mean-Analytic_std.error, ymax=Analytic_mean+Analytic_std.error),fill = 'dodgerblue3', alpha=0.2) +
  ggtitle("Analytic Thinking") +
  labs(x = "Month", y = 'Standardized score') +
  plot_aes + #here's our plot aes object
  geom_vline(xintercept = as.numeric(as.Date("2020-03-01")), linetype = 2) +
  geom_rect(data = df2, #summer surge
            aes(xmin = as.Date("2020-06-15", "%Y-%m-%d"), 
                xmax = as.Date("2020-07-20",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009) +
  geom_rect(data = df2, #winter surge
            aes(xmin = as.Date("2020-11-15", "%Y-%m-%d"), 
                xmax = as.Date("2021-01-01",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009)
Analytic_CEO <- Analytic_CEO + annotate(geom="text",x=as.Date("2020-07-01"),
                                y=50,label="Summer 2020 surge", size = 3) + 
  annotate(geom="text",x=as.Date("2020-12-03"),
           y=43,label="Winter 2020 surge", size = 3)

Cogproc

Cogproc <- 
  ggplot() +
  geom_line(data = df2, aes(x = Date_mean, y = cogproc_mean, color = "CEO"), #set our colors 
              linetype = 'solid') +
  geom_ribbon(data = df2, aes(x = Date_mean,ymin=cogproc_mean-cogproc_std.error, ymax=cogproc_mean+cogproc_std.error, fill = "CEO"), alpha=0.2) +
geom_line(data = reddit_tidy_2, aes(x = Date_mean, y = cogproc_mean, color = "Reddit"), #set our colors 
              linetype = 'solid') +
geom_ribbon(data = reddit_tidy_2, aes(x = Date_mean,ymin=cogproc_mean-cogproc_std.error, ymax=cogproc_mean+cogproc_std.error, fill = "Reddit"), alpha=0.2) +
  scale_x_date(date_breaks = "3 month", date_labels = "%Y-%m") +
  ggtitle("Cognitive Processing") +
  labs(x = "Month", y = '% of Total Words') +
  plot_aes + #here's our plot aes object
  geom_vline(xintercept = as.numeric(as.Date("2020-03-01")), linetype = 2) +
  geom_rect(data = df2, #summer surge
            aes(xmin = as.Date("2020-06-15", "%Y-%m-%d"), 
                xmax = as.Date("2020-07-20",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009) +
  geom_rect(data = df2, #winter surge
            aes(xmin = as.Date("2020-11-15", "%Y-%m-%d"), 
                xmax = as.Date("2021-01-01",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009)

Cogproc <- Cogproc + annotate(geom="text",x=as.Date("2020-07-01"),
                                y=12.2,label="Summer 2020 surge", size = 5) + 
  annotate(geom="text",x=as.Date("2020-12-03"),
           y=12.3,label="Winter 2020 surge", size = 5) 

Cogproc <- Cogproc +
  scale_color_manual(values = c(CEO = "dodgerblue3", Reddit = "red")) +
  scale_fill_manual(values = c(CEO = "dodgerblue3", Reddit = "red"))

Cogproc

CEO_Cogproc <- ggplot(data=df2, aes(x=Date_mean, y=cogproc_mean, group=1)) +
  geom_line(colour = "dodgerblue3") +
  scale_x_date(date_breaks = "3 month", date_labels = "%Y-%m") +
  geom_ribbon(aes(ymin=cogproc_mean-cogproc_std.error, ymax=cogproc_mean+cogproc_std.error),fill = 'dodgerblue3', alpha=0.2) +
  ggtitle("Cognitive Processing") +
  labs(x = "Month", y = '% Total Words') +
  plot_aes + #here's our plot aes object
  geom_vline(xintercept = as.numeric(as.Date("2020-03-01")), linetype = 1) +
  geom_rect(data = df2, #summer surge
            aes(xmin = as.Date("2020-06-15", "%Y-%m-%d"), 
                xmax = as.Date("2020-07-20",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009) +
  geom_rect(data = df2, #winter surge
            aes(xmin = as.Date("2020-11-15", "%Y-%m-%d"), 
                xmax = as.Date("2021-01-01",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009)
CEO_Cogproc <- CEO_Cogproc + annotate(geom="text",x=as.Date("2020-07-01"),
                                y=12.5,label="Summer 2020 surge", size = 3) + 
  annotate(geom="text",x=as.Date("2020-12-03"),
           y=12.5,label="Winter 2020 surge", size = 3)

I words

i <- 
  ggplot() +
  geom_line(data = df2, aes(x = Date_mean, y = i_mean, color = "CEO"), #set our colors 
              linetype = 'solid') +
  geom_ribbon(data = df2, aes(x = Date_mean,ymin=i_mean-i_std.error, ymax=i_mean+i_std.error, fill = "CEO"), alpha=0.2) +
geom_line(data = reddit_tidy_2, aes(x = Date_mean, y = i_mean, color = "Reddit"), #set our colors 
              linetype = 'solid') +
geom_ribbon(data = reddit_tidy_2, aes(x = Date_mean,ymin=i_mean-i_std.error, ymax=i_mean+i_std.error, fill = "Reddit"), alpha=0.2) +
  scale_x_date(date_breaks = "3 month", date_labels = "%Y-%m") +
  ggtitle("I-usage") +
  labs(x = "Month", y = '% of Total Words') +
  plot_aes + #here's our plot aes object
  geom_vline(xintercept = as.numeric(as.Date("2020-03-01")), linetype = 2) +
  geom_rect(data = df2, #summer surge
            aes(xmin = as.Date("2020-06-15", "%Y-%m-%d"), 
                xmax = as.Date("2020-07-20",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009) +
  geom_rect(data = df2, #winter surge
            aes(xmin = as.Date("2020-11-15", "%Y-%m-%d"), 
                xmax = as.Date("2021-01-01",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009)

i <- i + annotate(geom="text",x=as.Date("2020-07-01"),
                                y=2.25,label="Summer 2020 surge", size = 5) + 
  annotate(geom="text",x=as.Date("2020-12-03"),
           y=1.95,label="Winter 2020 surge", size = 5) 

i <- i +
  scale_color_manual(values = c(CEO = "dodgerblue3", Reddit = "red")) +
  scale_fill_manual(values = c(CEO = "dodgerblue3", Reddit = "red"))
i

CEO_i <- ggplot(data=df2, aes(x=Date_mean, y=i_mean, group=1)) +
  geom_line(colour = "dodgerblue3") +
  scale_x_date(date_breaks = "3 month", date_labels = "%Y-%m") +
  geom_ribbon(aes(ymin=i_mean-i_std.error, ymax=i_mean+i_std.error),fill = 'dodgerblue3', alpha=0.2) +
  ggtitle("I-usage") +
  labs(x = "Month", y = '% Total Words') +
  plot_aes + #here's our plot aes object
  geom_vline(xintercept = as.numeric(as.Date("2020-03-01")), linetype = 1) +
  geom_rect(data = df2, #summer surge
            aes(xmin = as.Date("2020-06-15", "%Y-%m-%d"), 
                xmax = as.Date("2020-07-20",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009) +
  geom_rect(data = df2, #winter surge
            aes(xmin = as.Date("2020-11-15", "%Y-%m-%d"), 
                xmax = as.Date("2021-01-01",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009)
CEO_i <- CEO_i + annotate(geom="text",x=as.Date("2020-07-01"),
                                y=1.95,label="Summer 2020 surge", size = 3) + 
  annotate(geom="text",x=as.Date("2020-12-03"),
           y=1.95,label="Winter 2020 surge", size = 3)

We words

we <- 
  ggplot() +
  geom_line(data = df2, aes(x = Date_mean, y = we_mean, color = "CEO"), #set our colors 
              linetype = 'solid') +
  geom_ribbon(data = df2, aes(x = Date_mean,ymin=we_mean-we_std.error, ymax=we_mean+we_std.error, fill = "CEO"), alpha=0.2) +
geom_line(data = reddit_tidy_2, aes(x = Date_mean, y = we_mean, color = "Reddit"), #set our colors 
              linetype = 'solid') +
geom_ribbon(data = reddit_tidy_2, aes(x = Date_mean,ymin=we_mean-we_std.error, ymax=we_mean+we_std.error, fill = "Reddit"), alpha=0.2) +
  scale_x_date(date_breaks = "3 month", date_labels = "%Y-%m") +
  ggtitle("We-usage") +
  labs(x = "Month", y = '% of Total Words') +
  plot_aes + #here's our plot aes object
  geom_vline(xintercept = as.numeric(as.Date("2020-03-01")), linetype = 2) +
  geom_rect(data = df2, #summer surge
            aes(xmin = as.Date("2020-06-15", "%Y-%m-%d"), 
                xmax = as.Date("2020-07-20",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009) +
  geom_rect(data = df2, #winter surge
            aes(xmin = as.Date("2020-11-15", "%Y-%m-%d"), 
                xmax = as.Date("2021-01-01",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009)

we <- we + annotate(geom="text",x=as.Date("2020-07-01"),
                                y=6.5,label="Summer 2020 surge", size = 5) + 
  annotate(geom="text",x=as.Date("2020-12-03"),
           y=6,label="Winter 2020 surge", size = 5) 
we <- we +
  scale_color_manual(values = c(CEO = "dodgerblue3", Reddit = "red")) +
  scale_fill_manual(values = c(CEO = "dodgerblue3", Reddit = "red"))
we

CEO_we <- ggplot(data=df2, aes(x=Date_mean, y=we_mean, group=1)) +
  geom_line(colour = "dodgerblue3") +
  scale_x_date(date_breaks = "3 month", date_labels = "%Y-%m") +
  geom_ribbon(aes(ymin=we_mean-we_std.error, ymax=we_mean+we_std.error),fill = 'dodgerblue3', alpha=0.2) +
  ggtitle("We-usage") +
  labs(x = "Month", y = '% Total Words') +
  plot_aes + #here's our plot aes object
  geom_vline(xintercept = as.numeric(as.Date("2020-03-01")), linetype = 1) +
  geom_rect(data = df2, #summer surge
            aes(xmin = as.Date("2020-06-15", "%Y-%m-%d"), 
                xmax = as.Date("2020-07-20",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009) +
  geom_rect(data = df2, #winter surge
            aes(xmin = as.Date("2020-11-15", "%Y-%m-%d"), 
                xmax = as.Date("2021-01-01",  "%Y-%m-%d"),
                ymin = -Inf, 
                ymax = Inf),
            fill = "gray", 
            alpha = 0.009)
CEO_we <- CEO_we + annotate(geom="text",x=as.Date("2020-07-01"),
                                y=6.5,label="Summer 2020 surge", size = 3) + 
  annotate(geom="text",x=as.Date("2020-12-03"),
           y=6.5,label="Winter 2020 surge", size = 3)

Tie them all together

graphs <- ggpubr::ggarrange(Analytic,Cogproc,i,we,ncol=2, nrow=2, common.legend = TRUE, legend = "bottom")
annotate_figure(graphs,
                top = text_grob("CEOs' Language Change",  color = "black", face = "bold", size = 20))

CEO Only Graphs

graphs <- ggpubr::ggarrange(Analytic_CEO,CEO_Cogproc,CEO_i,CEO_we ,ncol=2, nrow=2, common.legend = TRUE, legend = "bottom")
annotate_figure(graphs,
                top = text_grob("CEOs' Language Change",  color = "black", face = "bold", size = 20))

Exploratory Analyses

df <-read_csv("https://raw.githubusercontent.com/scm1210/Language_Lab_Repro/main/Big_CEO.csv") #read in the data from github 
#put code here to read in Big CEO data
df <- df %>% filter(WC<=5400)   %>% 
  filter(WC>=25)

df$month_year <- format(as.Date(df$Date), "%Y-%m") ###extracting month and year to build fiscal quarter graphs, need a new variable bc if not it'll give us issues

df <- df["2010-01-01"<= df$Date & df$Date <= "2021-04-01",] #subsetting covid dates 

data_tidy <- df %>% dplyr::select(Date, Speaker, Analytic, cogproc,allnone,we,i,emo_anx) %>%
  mutate(Date = lubridate::ymd(Date),
         time_month = as.numeric(Date - ymd("2010-03-01")) /91, #centering at start of Q1 2020, divide by 91 bc ~ number days in a quarter 
         time_month_quad = time_month * time_month) #making our quadratic term, which is not needed

data_tidy$month_year <- format(as.Date(data_tidy$Date), "%Y-%m") ###extracting month and year to build fiscal quarter graphs, need a new variable bc if not it'll give us issues
data_tidy$Date_off <- floor(data_tidy$time_month) #rounding off dates to whole months using floor function (0 = Q1 2020)
data_tidy$Date_covid <- as.factor(data_tidy$Date_off) #factorize

Load in Reddit data

reddit <- read_csv("~/Dropbox/CEO-data/LIWC-15-Data/LIWC_BLMProject_22cities_01012016-04302021_weeklyavg.csv")

reddit <- reddit %>%
  mutate(month_year = format(Week, "%Y-%m"))

### clean and tidy reddit data 

#reddit$month_year <- as.Date(reddit$month_year)

reddit$month_year <- format(as.Date(reddit$Week), "%Y-%m") ###extracting month and year to build fiscal quarter graphs, need a new variable bc if not it'll give us issues

reddit <- reddit %>% filter(WC<=5400)   %>% 
  filter(WC>=25)

reddit_tidy <- reddit %>%#converting our dates to quarterly dates 
  group_by(month_year) %>% ###grouping by the Top100 tag and date 
  summarise_at(vars("Week","WC", "Analytic","we","i",'insight'),  funs(mean, std.error),) #pulling the means and SEs for our variables of interest

reddit_tidy <- reddit_tidy[reddit_tidy$month_year <= "2021-04",] #subsetting covid dates 

Write our Table Funcions

baseline_ttest <- function(ttest_list) {
  n <- length(ttest_list)  # Get the length of ttest_list
  
  # Extract relevant information from each test and store in a data frame
  ttest_df <- data.frame(
    Group1 = rep(39, n),
    Group2 = seq(-1, 44, 1)[1:n],
    t = sapply(ttest_list, function(x) paste0("t(", round(x$parameter, 3), ") = ", round(x$statistic, 3))),
    p_value = sapply(ttest_list, function(x) x$p.value)
  )
  
  # Format p-values as scientific notation
  ttest_df$p_value <- format(ttest_df$p_value, scientific = TRUE)
  
  # Rename columns
  colnames(ttest_df) <- c("Group1", "Group2", "t-statistic", "p-value")
  
  # Create table using kableExtra
  kable(ttest_df, caption = "Summary of Welch's t-Tests", booktabs = TRUE) %>%
   kableExtra::kable_styling()
}



baseline_cohen_d <- function(cohen_d_list) {
  n <- length(cohen_d_list)  # Get the length of cohen_d_list
  
  # Extract relevant information from each test and store in a data frame
  cohen_d_df <- data.frame(
    `t` = rep(39, n),
    `t + 1` = seq(-1, 44, 1)[1:n],
    `Cohen's d` = sapply(cohen_d_list, function(x) {
      if (is.atomic(x)) {
        return(x)
      } else {
        return(x$estimate)
      }
    })
  )
  
  # Rename columns
  colnames(cohen_d_df) <- c("t", "t + 1", "Cohen's d")
  
  # Create table using kableExtra
  kable(cohen_d_df, caption = "Summary of Cohen's D", booktabs = TRUE) %>%
    kableExtra::kable_styling()
}

baseline_mean_diff <- function(mean_diff_list) {
  n <- length(mean_diff_list)  # Get the length of mean_diff_list
  
  # Extract relevant information from each mean difference calculation and store in a data frame
  mean_diff_df <- data.frame(
    Group1 = rep(39, n),
    Group2 = seq(-1, 44, 1)[1:n],
    `Mean Difference` = mean_diff_list
  )
  
  # Rename columns
  colnames(mean_diff_df) <- c("t", "t+1", "Mean Difference")
  
  # Create table using kableExtra
  kable(mean_diff_df, caption = "Summary of Mean Differences", booktabs = TRUE) %>%
    kableExtra::kable_styling()
}

Write our Stats Funcions

Analytic Thinking

analytic_my.t = function(fac1, fac2){
  t.test(data_tidy$Analytic[data_tidy$Date_covid==fac1], 
         data_tidy$Analytic[data_tidy$Date_covid==fac2])
} #writing our t-test function to compare t to t[i] 

analytic_my.d = function(fac1, fac2){
  cohen.d(data_tidy$Analytic[data_tidy$Date_covid==fac1], 
          data_tidy$Analytic[data_tidy$Date_covid==fac2])
} #function for cohen's d

analytic_mean <-  function(fac1, fac2){
  mean(data_tidy$Analytic[data_tidy$Date_covid==fac1])- 
    mean(data_tidy$Analytic[data_tidy$Date_covid==fac2])
} #function to do mean differences

Cognitive Processing

cogproc_my.t = function(fac1, fac2){
  t.test(data_tidy$cogproc[data_tidy$Date_covid==fac1], 
         data_tidy$cogproc[data_tidy$Date_covid==fac2])
} #writing our t-test function to compare t to t[i] 


cogproc_my.d = function(fac1, fac2){
  cohen.d(data_tidy$cogproc[data_tidy$Date_covid==fac1], 
          data_tidy$cogproc[data_tidy$Date_covid==fac2])
} #function for cohen's d

cogproc_mean <-  function(fac1, fac2){
  mean(data_tidy$cogproc[data_tidy$Date_covid==fac1])- 
    mean(data_tidy$cogproc[data_tidy$Date_covid==fac2])
} #function to do mean differences

I-words

i_my.t = function(fac1, fac2){
  t.test(data_tidy$i[data_tidy$Date_covid==fac1], 
         data_tidy$i[data_tidy$Date_covid==fac2])
} #writing our t-test function to compare t to t + 1 

i_my.d = function(fac1, fac2){
  cohen.d(data_tidy$i[data_tidy$Date_covid==fac1], 
          data_tidy$i[data_tidy$Date_covid==fac2])
} #function for cohen's d


i_mean <-  function(fac1, fac2){
  mean(data_tidy$i[data_tidy$Date_covid==fac1])- 
    mean(data_tidy$i[data_tidy$Date_covid==fac2])
} #function to do mean differences

We-words

we_my.t = function(fac1, fac2){
  t.test(data_tidy$we[data_tidy$Date_covid==fac1], 
         data_tidy$we[data_tidy$Date_covid==fac2])
} 

we_my.d = function(fac1, fac2){
  cohen.d(data_tidy$we[data_tidy$Date_covid==fac1], 
          data_tidy$we[data_tidy$Date_covid==fac2])
} #function for cohen's d

we_mean <-  function(fac1, fac2){
  mean(data_tidy$we[data_tidy$Date_covid==fac1])- 
    mean(data_tidy$we[data_tidy$Date_covid==fac2])
} #function to do mean differences

Analyses

Analytic Thining

T-test

analytic_ttest_baseline <-mapply(analytic_my.t,39, seq(1,44,1),SIMPLIFY=FALSE) #compare t (first parantheses) to t[i] (second parentheses)increasing by 1
baseline_ttest(analytic_ttest_baseline)

Summary of Welch’s t-Tests

Group1	Group2	t-statistic	p-value
39	-1	t(1398.238) = -3.958	7.935887e-05
39	0	t(1316.759) = -2.829	4.743809e-03
39	1	t(1104.194) = -4.448	9.563828e-06
39	2	t(1326.762) = -3.111	1.903600e-03
39	3	t(1700.928) = -1.482	1.384678e-01
39	4	t(1728.557) = -0.774	4.388235e-01
39	5	t(1638.866) = -2.607	9.224253e-03
39	6	t(1889.515) = -1.85	6.444644e-02
39	7	t(2364.197) = -2.444	1.459342e-02
39	8	t(2496.556) = -0.954	3.399879e-01
39	9	t(2555.646) = -2.34	1.935830e-02
39	10	t(3156.363) = -1.073	2.835575e-01
39	11	t(3065.339) = -1.384	1.665191e-01
39	12	t(3004.136) = -0.423	6.724037e-01
39	13	t(2715.245) = -2.739	6.193577e-03
39	14	t(3089.695) = -2.773	5.592719e-03
39	15	t(3047.279) = -2.26	2.391210e-02
39	16	t(3074.353) = -1.681	9.278904e-02
39	17	t(2762.79) = -3.086	2.047006e-03
39	18	t(3018.943) = -2.858	4.298007e-03
39	19	t(3050.245) = -1.99	4.672003e-02
39	20	t(3023.232) = -0.22	8.260843e-01
39	21	t(2747.907) = -0.775	4.381557e-01
39	22	t(2981.893) = -1.599	1.099204e-01
39	23	t(3090.796) = -1.463	1.434427e-01
39	24	t(3047.223) = -1.777	7.564602e-02
39	25	t(2754.568) = -1.784	7.460686e-02
39	26	t(2935.258) = -2.214	2.690626e-02
39	27	t(3100.911) = -1.71	8.734543e-02
39	28	t(3084.53) = -0.391	6.956618e-01
39	29	t(2764.156) = -1.613	1.067840e-01
39	30	t(2860.302) = -0.985	3.245110e-01
39	31	t(3026.977) = -1.256	2.092528e-01
39	32	t(3071.939) = -0.182	8.559538e-01
39	33	t(2771.37) = -1.342	1.797001e-01
39	34	t(2808.116) = -1.902	5.732281e-02
39	35	t(3027.247) = -1.259	2.082164e-01
39	36	t(3010.301) = -0.285	7.753956e-01
39	37	t(2760) = 0	1.000000e+00
39	38	t(2689.448) = 3.751	1.795358e-04
39	39	t(2962.494) = 2.376	1.754338e-02
39	40	t(3041.46) = 2.343	1.919049e-02
39	41	t(2599.104) = 1.854	6.387322e-02
39	42	t(2701.291) = 0.504	6.141739e-01

Cohen’s D

analytic_D_baseline <- mapply(analytic_my.d,39, seq(1,44,1),SIMPLIFY=FALSE) 
baseline_cohen_d(analytic_D_baseline)

Summary of Cohen’s D

t	t + 1	Cohen’s d
39	-1	-0.1851029
39	0	-0.1339318
39	1	-0.2227250
39	2	-0.1475299
39	3	-0.0657255
39	4	-0.0341759
39	5	-0.1166355
39	6	-0.0796613
39	7	-0.0983884
39	8	-0.0377881
39	9	-0.0918577
39	10	-0.0364593
39	11	-0.0492566
39	12	-0.0151757
39	13	-0.1048534
39	14	-0.0956737
39	15	-0.0797918
39	16	-0.0588432
39	17	-0.1173865
39	18	-0.0943563
39	19	-0.0693780
39	20	-0.0076498
39	21	-0.0295652
39	22	-0.0517852
39	23	-0.0503695
39	24	-0.0612642
39	25	-0.0679003
39	26	-0.0708322
39	27	-0.0580598
39	28	-0.0132602
39	29	-0.0613006
39	30	-0.0310062
39	31	-0.0424310
39	32	-0.0060546
39	33	-0.0509440
39	34	-0.0587861
39	35	-0.0420544
39	36	-0.0095347
39	37	0.0000000
39	38	0.1181953
39	39	0.0790406
39	40	0.0778936
39	41	0.0723611
39	42	0.0193511

Mean Differences

analytic_mean_baseline <- mapply(analytic_mean, 39, seq(1,44,1)) #across all of the months comparing to time zero
baseline_mean_diff(analytic_mean_baseline)

Summary of Mean Differences

t	t+1	Mean Difference
39	-1	-2.5595130
39	0	-1.8237112
39	1	-3.1031328
39	2	-2.0355221
39	3	-0.9202096
39	4	-0.4735812
39	5	-1.5973706
39	6	-1.0953330
39	7	-1.3665653
39	8	-0.5164610
39	9	-1.2581615
39	10	-0.5232129
39	11	-0.6991442
39	12	-0.2083640
39	13	-1.4530301
39	14	-1.3405792
39	15	-1.1050628
39	16	-0.8215474
39	17	-1.5740651
39	18	-1.3251540
39	19	-0.9586343
39	20	-0.1047645
39	21	-0.4018354
39	22	-0.7327220
39	23	-0.7061372
39	24	-0.8469288
39	25	-0.9293251
39	26	-1.0042991
39	27	-0.8187721
39	28	-0.1862589
39	29	-0.8430907
39	30	-0.4408389
39	31	-0.5884785
39	32	-0.0854432
39	33	-0.6972308
39	34	-0.8428210
39	35	-0.5871033
39	36	-0.1326276
39	37	0.0000000
39	38	1.6383487
39	39	1.0925364
39	40	1.0940622
39	41	0.9841566
39	42	0.2666881

Cogproc

T-test

cogproc_ttest_baseline <- mapply(cogproc_my.t, 39, seq(-1,44,1),SIMPLIFY=FALSE) #compare t (first parathese) to t[i] (second parantheses) increasing by 1
baseline_ttest(cogproc_ttest_baseline)

Summary of Welch’s t-Tests

Group1	Group2	t-statistic	p-value
39	-1	t(1596.222) = -2.415	1.584343e-02
39	0	t(1896.147) = -3.384	7.292514e-04
39	1	t(1569.956) = -3.665	2.557183e-04
39	2	t(1448.795) = -2.554	1.074468e-02
39	3	t(1268.938) = -1.709	8.770750e-02
39	4	t(1432.206) = -3.144	1.702590e-03
39	5	t(1903.641) = -4.841	1.392629e-06
39	6	t(1856.011) = -4.492	7.501149e-06
39	7	t(1671.938) = -2.555	1.070559e-02
39	8	t(2041.807) = -2.55	1.083707e-02
39	9	t(2448.976) = -3.049	2.321103e-03
39	10	t(2515.99) = -3.439	5.924926e-04
39	11	t(2590.933) = -2.754	5.920130e-03
39	12	t(2917.857) = -3.834	1.285412e-04
39	13	t(3005.391) = -4.139	3.576382e-05
39	14	t(2878.554) = -3.362	7.848321e-04
39	15	t(2729.533) = -1.394	1.634747e-01
39	16	t(2937.206) = -2.929	3.426514e-03
39	17	t(2930.779) = -2.068	3.871690e-02
39	18	t(2974.592) = -1.231	2.183927e-01
39	19	t(2750.003) = -1.093	2.745876e-01
39	20	t(2879.244) = -2.64	8.338538e-03
39	21	t(2826.016) = -1.209	2.268931e-01
39	22	t(2872.258) = -1.939	5.256728e-02
39	23	t(2733.175) = -1.48	1.390976e-01
39	24	t(2747.232) = -2.891	3.865562e-03
39	25	t(2933.786) = -1.07	2.846932e-01
39	26	t(2953.155) = -0.8	4.238448e-01
39	27	t(2746.072) = -2.129	3.331096e-02
39	28	t(2750.139) = -1.545	1.223460e-01
39	29	t(2880.016) = -0.7	4.841564e-01
39	30	t(2979.921) = -1.257	2.087883e-01
39	31	t(2757.621) = -0.795	4.265726e-01
39	32	t(2568.527) = -1.64	1.010420e-01
39	33	t(2892.73) = -0.589	5.559665e-01
39	34	t(2905.335) = -1.252	2.107755e-01
39	35	t(2759.555) = 0.285	7.756173e-01
39	36	t(2574.395) = -0.612	5.403712e-01
39	37	t(2847.493) = -0.568	5.702390e-01
39	38	t(2921.523) = -0.641	5.215064e-01
39	39	t(2760) = 0	1.000000e+00
39	40	t(2455.193) = -6.888	7.144573e-12
39	41	t(2772.368) = -6.631	3.987064e-11
39	42	t(2795.825) = -4.044	5.404755e-05
39	43	t(2633.899) = -0.338	7.354177e-01
39	44	t(2717.576) = -1.687	9.172727e-02

Cohen’s D

cogproc_D_baseline <- mapply(cogproc_my.d,39, seq(-1,44,1),SIMPLIFY=FALSE)
baseline_cohen_d(cogproc_D_baseline)

Summary of Cohen’s D

t	t + 1	Cohen’s d
39	-1	-0.1082032
39	0	-0.1452980
39	1	-0.1644938
39	2	-0.1166419
39	3	-0.0807181
39	4	-0.1449077
39	5	-0.2075203
39	6	-0.1940441
39	7	-0.1136000
39	8	-0.1073236
39	9	-0.1217299
39	10	-0.1359451
39	11	-0.1077753
39	12	-0.1335815
39	13	-0.1483548
39	14	-0.1219518
39	15	-0.0533229
39	16	-0.1026109
39	17	-0.0738308
39	18	-0.0435087
39	19	-0.0415667
39	20	-0.0885809
39	21	-0.0430370
39	22	-0.0684869
39	23	-0.0563917
39	24	-0.0964562
39	25	-0.0374106
39	26	-0.0278350
39	27	-0.0810604
39	28	-0.0506828
39	29	-0.0243024
39	30	-0.0430806
39	31	-0.0302226
39	32	-0.0539653
39	33	-0.0201837
39	34	-0.0425144
39	35	0.0108260
39	36	-0.0196669
39	37	-0.0193508
39	38	-0.0216342
39	39	0.0000000
39	40	-0.2262115
39	41	-0.2255093
39	42	-0.1381970
39	43	-0.0131311
39	44	-0.0646561

Mean Differences

cogproc_mean_baseline <- mapply(cogproc_mean, 39, seq(-1,44,1)) # comparing time zero [3/2020]across all of the months
baseline_mean_diff(cogproc_meandiff)

Summary of Mean Differences

t	t+1	Mean Difference
39	-1	-0.6107287
39	0	0.1784774
39	1	0.6094504
39	2	-0.6540232
39	3	0.1559844
39	4	0.7442075
39	5	-0.2962170
39	6	-0.2746360
39	7	0.5304979
39	8	-0.5357971
39	9	0.2775877
39	10	-0.0886600

I-words

T-test

i_ttest_baseline <- mapply(i_my.t, 39, seq(-1,44,1),SIMPLIFY=FALSE) #compare t (first paratheseses) to t[i] (second parentheses) increasing by 1
baseline_ttest(i_ttest_baseline)

Summary of Welch’s t-Tests

Group1	Group2	t-statistic	p-value
39	-1	t(1552.097) = -0.547	5.847291e-01
39	0	t(1891.692) = 0.927	3.540054e-01
39	1	t(1543.548) = -1.288	1.980085e-01
39	2	t(1384.86) = -0.109	9.135023e-01
39	3	t(1243.226) = -1.623	1.048443e-01
39	4	t(1406.99) = -0.995	3.199685e-01
39	5	t(1807.256) = -2.507	1.226620e-02
39	6	t(1839.806) = -3.324	9.032501e-04
39	7	t(1676.736) = -2.53	1.148118e-02
39	8	t(1968.571) = -2.1	3.587236e-02
39	9	t(2437.139) = -1.237	2.163517e-01
39	10	t(2522.069) = -1.346	1.784795e-01
39	11	t(2584.073) = -1.162	2.454416e-01
39	12	t(2968.434) = -1.419	1.558820e-01
39	13	t(3026.77) = -2.397	1.657169e-02
39	14	t(2993.59) = -2.067	3.878505e-02
39	15	t(2732) = -0.717	4.734852e-01
39	16	t(2965.139) = -0.102	9.187266e-01
39	17	t(3003.951) = 0.591	5.547413e-01
39	18	t(2941.793) = -0.298	7.653434e-01
39	19	t(2746.3) = 1.066	2.866121e-01
39	20	t(3052.212) = 1.024	3.060977e-01
39	21	t(2945.021) = 1.199	2.304594e-01
39	22	t(2914.983) = 0.722	4.704495e-01
39	23	t(2749.491) = -0.7	4.839122e-01
39	24	t(2859.859) = 1.32	1.870804e-01
39	25	t(2961.854) = 1.542	1.232059e-01
39	26	t(2959.922) = 0.898	3.692053e-01
39	27	t(2754.67) = -1.204	2.286463e-01
39	28	t(2796.851) = 1.861	6.285424e-02
39	29	t(2950.089) = 1.04	2.984570e-01
39	30	t(2957.818) = 0.508	6.116638e-01
39	31	t(2761.248) = -2.113	3.466039e-02
39	32	t(2623.154) = 1.589	1.122499e-01
39	33	t(2902.226) = 1.451	1.468228e-01
39	34	t(2837.131) = 1.36	1.740380e-01
39	35	t(2772.998) = 0.273	7.845833e-01
39	36	t(2685.979) = 1.471	1.413725e-01
39	37	t(2847.315) = 1.777	7.574039e-02
39	38	t(2842.537) = 0.368	7.130643e-01
39	39	t(2760) = 0	1.000000e+00
39	40	t(2443.982) = -0.094	9.249572e-01
39	41	t(2883.506) = -2.165	3.046355e-02
39	42	t(2869.361) = -1.774	7.624031e-02
39	43	t(2624.604) = -2.188	2.878893e-02
39	44	t(2714.734) = 0.937	3.486622e-01

Cohen’s D

i_D_baseline <- mapply(i_my.d, 39, seq(-1,44,1),SIMPLIFY=FALSE)
baseline_cohen_d(i_D_baseline)

Summary of Cohen’s D

t	t + 1	Cohen’s d
39	-1	-0.0247279
39	0	0.0398365
39	1	-0.0581512
39	2	-0.0050462
39	3	-0.0772991
39	4	-0.0461594
39	5	-0.1091999
39	6	-0.1440105
39	7	-0.1124059
39	8	-0.0893624
39	9	-0.0494378
39	10	-0.0531632
39	11	-0.0454914
39	12	-0.0492032
39	13	-0.0857292
39	14	-0.0742666
39	15	-0.0274178
39	16	-0.0035654
39	17	0.0209493
39	18	-0.0105813
39	19	0.0405406
39	20	0.0336718
39	21	0.0422507
39	22	0.0253916
39	23	-0.0266926
39	24	0.0433859
39	25	0.0537619
39	26	0.0312322
39	27	-0.0458412
39	28	0.0606377
39	29	0.0358631
39	30	0.0174393
39	31	-0.0802960
39	32	0.0517999
39	33	0.0496875
39	34	0.0465307
39	35	0.0103788
39	36	0.0463627
39	37	0.0605520
39	38	0.0125214
39	39	0.0000000
39	40	-0.0031002
39	41	-0.0726695
39	42	-0.0601078
39	43	-0.0851660
39	44	0.0359521

Mean Differences

i_mean_baseline <- mapply(i_mean, 39, seq(1,44,1)) # comparing time zero [3/2020]across all of the months
baseline_mean_diff(i_mean_baseline)

Summary of Mean Differences

t	t+1	Mean Difference
39	-1	-0.0501342
39	0	-0.0043782
39	1	-0.0669225
39	2	-0.0403834
39	3	-0.0961358
39	4	-0.1254502
39	5	-0.0988873
39	6	-0.0779165
39	7	-0.0430919
39	8	-0.0464607
39	9	-0.0396123
39	10	-0.0431445
39	11	-0.0772021
39	12	-0.0658681
39	13	-0.0237140
39	14	-0.0031143
39	15	0.0185136
39	16	-0.0091472
39	17	0.0341431
39	18	0.0309884
39	19	0.0366076
39	20	0.0218302
39	21	-0.0233920
39	22	0.0385859
39	23	0.0469517
39	24	0.0272901
39	25	-0.0407296
39	26	0.0537960
39	27	0.0313834
39	28	0.0153213
39	29	-0.0721711
39	30	0.0447642
39	31	0.0432108
39	32	0.0399635
39	33	0.0091137
39	34	0.0417002
39	35	0.0522583
39	36	0.0108053
39	37	0.0000000
39	38	-0.0025900
39	39	-0.0638521
39	40	-0.0523031
39	41	-0.0732920
39	42	0.0314656

We-words

T-test

we_ttest_baseline <- mapply(we_my.t, 39, seq(-1,44,1),SIMPLIFY=FALSE) #compare t (first parathese) to t[i] (second parantheses) increasing by 1
baseline_ttest(we_ttest_baseline)

Summary of Welch’s t-Tests

Group1	Group2	t-statistic	p-value
39	-1	t(1465.793) = 4.671	3.279146e-06
39	0	t(1848.152) = 6.423	1.697612e-10
39	1	t(1470.592) = 6.634	4.571561e-11
39	2	t(1340.255) = 4.894	1.108406e-06
39	3	t(1186.083) = 4.105	4.315229e-05
39	4	t(1346.481) = 4.108	4.228869e-05
39	5	t(1834.74) = 4.329	1.576337e-05
39	6	t(1781.674) = 5.422	6.681106e-08
39	7	t(1634.64) = 4.157	3.391822e-05
39	8	t(1907.992) = 5.269	1.524353e-07
39	9	t(2378.729) = 4.998	6.210940e-07
39	10	t(2500.799) = 4.77	1.952074e-06
39	11	t(2535.466) = 4.063	4.986150e-05
39	12	t(3089.99) = 4.718	2.484294e-06
39	13	t(3056.043) = 5.46	5.155028e-08
39	14	t(3020.142) = 3.676	2.413218e-04
39	15	t(2708.481) = 3.988	6.850857e-05
39	16	t(3036.488) = 5.82	6.499374e-09
39	17	t(3068.469) = 4.267	2.040157e-05
39	18	t(3121.075) = 3.099	1.956938e-03
39	19	t(2762.422) = 4.309	1.693639e-05
39	20	t(3050.317) = 5.009	5.775021e-07
39	21	t(3014.948) = 4.539	5.866656e-06
39	22	t(3073.922) = 3.843	1.242059e-04
39	23	t(2746.502) = 3.824	1.344061e-04
39	24	t(2875.797) = 4.586	4.708222e-06
39	25	t(3081.404) = 3.337	8.573867e-04
39	26	t(3087.905) = 3.526	4.279508e-04
39	27	t(2757.36) = 4.105	4.154902e-05
39	28	t(2979.319) = 4.253	2.174582e-05
39	29	t(3056.368) = 4.106	4.135301e-05
39	30	t(3039.528) = 3.015	2.590510e-03
39	31	t(2767.115) = 3.101	1.949471e-03
39	32	t(2771.482) = 3.391	7.054067e-04
39	33	t(2979.963) = 3.003	2.698641e-03
39	34	t(3046.109) = 1.892	5.860198e-02
39	35	t(2770.676) = 0.404	6.863047e-01
39	36	t(2773.232) = 3.487	4.962671e-04
39	37	t(2990.159) = 2.309	2.101141e-02
39	38	t(2986.735) = 1.665	9.596844e-02
39	39	t(2760) = 0	1.000000e+00
39	40	t(2634.135) = 6.693	2.665399e-11
39	41	t(2865.847) = 9.071	2.130887e-19
39	42	t(2866.786) = 7.127	1.291552e-12
39	43	t(2619.501) = 4.263	2.089149e-05
39	44	t(2715.201) = 4.971	7.070835e-07

Cohen’s D

we_D_baseline <- mapply(we_my.d, 0, seq(-1,44,1),SIMPLIFY=FALSE)
baseline_cohen_d(we_D_baseline)

Summary of Cohen’s D

t	t + 1	Cohen’s d
39	-1	-0.0557103
39	0	0.0000000
39	1	0.0282300
39	2	-0.0463795
39	3	-0.0765264
39	4	-0.0785886
39	5	-0.0903266
39	6	-0.0369619
39	7	-0.0861466
39	8	-0.0471293
39	9	-0.0696648
39	10	-0.0852994
39	11	-0.1091741
39	12	-0.1044371
39	13	-0.0699960
39	14	-0.1377183
39	15	-0.1142513
39	16	-0.0696756
39	17	-0.1164580
39	18	-0.1531635
39	19	-0.1116030
39	20	-0.0985302
39	21	-0.1149236
39	22	-0.1345665
39	23	-0.1278698
39	24	-0.1211008
39	25	-0.1517493
39	26	-0.1449650
39	27	-0.1242977
39	28	-0.1244390
39	29	-0.1282285
39	30	-0.1664816
39	31	-0.1564665
39	32	-0.1608429
39	33	-0.1709072
39	34	-0.2024843
39	35	-0.2561414
39	36	-0.1541242
39	37	-0.1924580
39	38	-0.2139859
39	39	-0.2779748
39	40	-0.0613540
39	41	0.0272928
39	42	-0.0377803
39	43	-0.1145791
39	44	-0.0872167

Mean Differences

we_mean_baseline <- mapply(we_mean, 39, seq(-1,44,1)) # comparing time zero [3/2020]across all of the months
baseline_mean_diff(we_mean_baseline)

Summary of Mean Differences

t	t+1	Mean Difference
39	-1	0.3137668
39	0	0.3955121
39	1	0.4359992
39	2	0.3290490
39	3	0.2855660
39	4	0.2808382
39	5	0.2669796
39	6	0.3419809
39	7	0.2698555
39	8	0.3270471
39	9	0.2931861
39	10	0.2721081
39	11	0.2342147
39	12	0.2395374
39	13	0.2901721
39	14	0.1927559
39	15	0.2253730
39	16	0.2937306
39	17	0.2221707
39	18	0.1625054
39	19	0.2350808
39	20	0.2468167
39	21	0.2291996
39	22	0.1960384
39	23	0.2103506
39	24	0.2188209
39	25	0.1698311
39	26	0.1792838
39	27	0.2204439
39	28	0.2051901
39	29	0.2059084
39	30	0.1504079
39	31	0.1695979
39	32	0.1585671
39	33	0.1475049
39	34	0.0937065
39	35	0.0222390
39	36	0.1626729
39	37	0.1131470
39	38	0.0815058
39	39	0.0000000
39	40	0.3068743
39	41	0.4343882
39	42	0.3420502
39	43	0.2341940
39	44	0.2713253

Figures

df2<- df %>%
  mutate(Date=as.yearqtr(Date)) %>% #converting our dates to quarterly dates 
  group_by(Top100, Date) %>% ###grouping by the Top100 tag and date 
  summarise_at(vars("WC", "Analytic", "Clout", "Drives","cogproc","focuspast","focuspresent",
                    "focusfuture","power","allure","we","i",'insight','emo_anx','allnone',),  funs(mean, std.error),) #pulling the means and SEs for our variables of interest
df2 <- df2["2010 Q1"<= df2$Date & df2$Date <= "2021 Q1",]
df2 <- df2[!duplicated(df2[c('Date')]),] #need to run this to get rid of duplicates for dates created when we collaspe into quarterly dates


reddit_tidy <- reddit %>%
  mutate(Date=as.yearqtr(month_year)) %>% #converting our dates to quarterly dates 
  group_by(month_year) %>% ###grouping by the Top100 tag and date 
  summarise_at(vars("Week","WC", "Analytic", "Clout","cogproc","focuspast","focuspresent",
                    "focusfuture","power","we","i",'insight',),  funs(mean, std.error),) #pulling the means and SEs for our variables of interest

reddit_tidy <- reddit_tidy %>%
  mutate(Quarter = as.yearqtr(Week_mean))
reddit_tidy <- reddit_tidy[!duplicated(reddit_tidy[c('Quarter')]),] #need to run this to get rid of duplicates for dates created when we collaspe into quarterly dates

Analytic Thinking

analytic <- 
  ggplot() +
  geom_line(data = df2, aes(x = Date, y = Analytic_mean, color = "CEO"), #set our colors 
              linetype = 'solid') +
  geom_ribbon(data = df2, aes(x = Date,ymin=Analytic_mean-Analytic_std.error, ymax=Analytic_mean+Analytic_std.error, fill = "CEO"), alpha=0.2) +
geom_line(data = reddit_tidy, aes(x = Quarter, y = Analytic_mean, color = "Reddit"), #set our colors 
              linetype = 'solid') +
geom_ribbon(data = reddit_tidy, aes(x = Quarter,ymin=Analytic_mean-Analytic_std.error, ymax=Analytic_mean+Analytic_std.error, fill = "Reddit"), alpha=0.2) +
  ggtitle("Analytic Thinking") + 
  labs(x = "Month", y = 'Standardized score') +
  plot_aes + #here's our plot aes object
  scale_x_yearqtr(name="Fiscal Quarter",format = "%YQ%q", n= 5) + #change the N to change the number of tick marks
  geom_vline(xintercept = as.numeric(as.yearqtr("2020 Q1")), linetype=1, show.legend = T) +
  theme(axis.text.x=element_text(angle=45, hjust=1)) +
  theme(plot.title.position = 'plot', 
        plot.title = element_text(hjust = 0.5, face = "bold", size = 16)) + 
  theme(axis.text=element_text(size=16),
        axis.title=element_text(size=20,face="bold"))


analytic <- analytic + annotate("text", x = 2011.5 , y = 36,
         label = "paste(italic(d[2010]), \" = -0.0817\")", parse = TRUE,size = 5) + 
  annotate("text", x = 2015.5 , y = 36,label = "paste(italic(d[2015]), \" = -0.050\")", parse = TRUE,size = 5)

analytic <- analytic +
  scale_color_manual(values = c(CEO = "dodgerblue3", Reddit = "red")) +
  scale_fill_manual(values = c(CEO = "dodgerblue3", Reddit = "red"))

analytic

CEO_Analytic_decade <- ggplot(data=df2, aes(x=Date, y=Analytic_mean, group=1)) +
  geom_line(colour = "dodgerblue3") +
  #geom_point(size=.5) +
  #scale_x_date(date_breaks = "1 month", date_labels = "%Y-%m") +
  ggtitle("Analytic Thinking") + 
  geom_ribbon(aes(ymin=Analytic_mean-Analytic_std.error, ymax=Analytic_mean+Analytic_std.error),fill = 'dodgerblue3', alpha=0.2) +
  labs(x = "Month", y = 'Standardized score') +
  plot_aes + #here's our plot aes object
  scale_x_yearqtr(name="Fiscal Quarter",format = "%YQ%q", n= 5) + #change the N to change the number of tick marks
  geom_vline(xintercept = as.numeric(as.yearqtr("2020 Q1")), linetype=1, show.legend = T) +
  theme(axis.text.x=element_text(angle=45, hjust=1)) +
  theme(plot.title.position = 'plot', 
        plot.title = element_text(hjust = 0.5, face = "bold", size = 16)) + 
  theme(axis.text=element_text(size=16),
        axis.title=element_text(size=20,face="bold"))

CEO_Analytic_decade <- CEO_Analytic_decade + annotate("text", x = 2011.5 , y = 36,
         label = "paste(italic(d[2010]), \" = -0.0817\")", parse = TRUE,size = 5) + 
  annotate("text", x = 2015.5 , y = 36,label = "paste(italic(d[2015]), \" = -0.050\")", parse = TRUE,size = 5)

Cognitive Processing

cogproc_decade <- 
  ggplot() +
  geom_line(data = df2, aes(x = Date, y = cogproc_mean, color = "CEO"), #set our colors 
              linetype = 'solid') +
  geom_ribbon(data = df2, aes(x = Date,ymin=cogproc_mean-cogproc_std.error, ymax=cogproc_mean+cogproc_std.error, fill = "CEO"), alpha=0.2) +
geom_line(data = reddit_tidy, aes(x = Quarter, y = cogproc_mean, color = "Reddit"), #set our colors 
              linetype = 'solid') +
geom_ribbon(data = reddit_tidy, aes(x = Quarter,ymin=cogproc_mean-cogproc_std.error, ymax=cogproc_mean+cogproc_std.error, fill = "Reddit"), alpha=0.2) +
  ggtitle("Cognitive Processing") + 
  labs(x = "Fiscal Quarter", y = '% of Total Words') +
  plot_aes + #here's our plot aes object
  scale_x_yearqtr(name="Fiscal Quarter",format = "%YQ%q", n= 5) + #change the N to change the number of tick marks
  geom_vline(xintercept = as.numeric(as.yearqtr("2020 Q1")), linetype=1, show.legend = T) +
  theme(axis.text.x=element_text(angle=45, hjust=1)) +
  theme(plot.title.position = 'plot', 
        plot.title = element_text(hjust = 0.5, face = "bold", size = 16)) + 
  theme(axis.text=element_text(size=16),
        axis.title=element_text(size=20,face="bold"))


cogproc <- cogproc_decade + annotate("text", x = 2011.5 , y = 11.2,
                         label = "paste(italic(d[2010]), \" = -0.082\")", parse = TRUE,size = 5) + 
  annotate("text", x = 2015.5 , y = 11.2,label = "paste(italic(d[2015]), \" = -0.043\")", parse = TRUE, size = 5)

cogproc <- cogproc +
  scale_color_manual(values = c(CEO = "dodgerblue3", Reddit = "red")) +
  scale_fill_manual(values = c(CEO = "dodgerblue3", Reddit = "red"))

cogproc

ceo_cogproc_decade <- ggplot(data=df2, aes(x=Date, y=cogproc_mean, group=1)) +
  geom_line(colour = "dodgerblue3") +
  #geom_point() +
  #scale_x_date(date_breaks = "1 month", date_labels = "%Y-%m") +
  ggtitle("Cognitive Processing") + 
  plot_aes + #here's our plot aes object
  geom_ribbon(aes(ymin=cogproc_mean-cogproc_std.error, ymax=cogproc_mean+cogproc_std.error),fill = 'dodgerblue3', alpha=0.2) +
  labs(x = "Month", y = '% of total words') +
  scale_x_yearqtr(name="Fiscal Quarter",format = "%YQ%q", n= 5) + #change the N to change the number of tick marks
  geom_vline(xintercept = as.numeric(as.yearqtr("2020 Q1")), linetype=1, show.legend = T) +
  theme(axis.text.x=element_text(angle=45, hjust=1)) +
  theme(axis.text.x=element_text(angle=45, hjust=1)) +
  theme(plot.title.position = 'plot', 
        plot.title = element_text(hjust = 0.5, face = "bold", size = 16)) + 
  theme(axis.text=element_text(size=16),
        axis.title=element_text(size=20,face="bold"))


ceo_cogproc_decade <- ceo_cogproc_decade + annotate("text", x = 2011.5 , y = 11.2,
                         label = "paste(italic(d[2010]), \" = -0.082\")", parse = TRUE,size = 5) + 
  annotate("text", x = 2015.5 , y = 11.2,label = "paste(italic(d[2015]), \" = -0.043\")", parse = TRUE, size = 5)

I-usage

i_decade <- 
  ggplot() +
  geom_line(data = df2, aes(x = Date, y = i_mean, color = "CEO"), #set our colors 
              linetype = 'solid') +
  geom_ribbon(data = df2, aes(x = Date,ymin=i_mean-i_std.error, ymax=i_mean+i_std.error, fill = "CEO"), alpha=0.2) +
geom_line(data = reddit_tidy, aes(x = Quarter, y = i_mean, color = "Reddit"), #set our colors 
              linetype = 'solid') +
geom_ribbon(data = reddit_tidy, aes(x = Quarter,ymin=i_mean-i_std.error, ymax=i_mean+i_std.error, fill = "Reddit"), alpha=0.2) +
  ggtitle("I-usage") + 
  labs(x = "Fiscal Quarter", y = '% of Total Words') +
  plot_aes + #here's our plot aes object
  scale_x_yearqtr(name="Fiscal Quarter",format = "%YQ%q", n= 5) + #change the N to change the number of tick marks
  geom_vline(xintercept = as.numeric(as.yearqtr("2020 Q1")), linetype=1, show.legend = T) +
  theme(axis.text.x=element_text(angle=45, hjust=1)) +
  theme(plot.title.position = 'plot', 
        plot.title = element_text(hjust = 0.5, face = "bold", size = 16)) + 
  theme(axis.text=element_text(size=16),
        axis.title=element_text(size=20,face="bold"))

i <- i_decade  + annotate("text", x = 2011.5 , y = 1.6,
                     label = "paste(italic(d[2010]), \" = -0.012\")", parse = TRUE,size = 5) + 
  annotate("text", x = 2015.5 , y = 1.6,label = "paste(italic(d[2015]), \" = 0.024\")", parse = TRUE,size = 5)

i <- i +
  scale_color_manual(values = c(CEO = "dodgerblue3", Reddit = "red")) +
  scale_fill_manual(values = c(CEO = "dodgerblue3", Reddit = "red"))

i

ceo_i_decade <- ggplot(data=df2, aes(x=Date, y=i_mean, group=1)) +
  geom_line(colour = "dodgerblue3") +
  #geom_point() +
  #scale_x_date(date_breaks = "1 month", date_labels = "%Y-%m") +
  ggtitle("Self-focus") + 
  plot_aes + #here's our plot aes object
  geom_ribbon(aes(ymin=i_mean-i_std.error, ymax=i_mean+i_std.error),fill = 'dodgerblue3', alpha=0.2) +
  labs(x = "Month", y = '% of total words') +
  scale_x_yearqtr(name="Fiscal Quarter",format = "%YQ%q", n= 5) + #change the N to change the number of tick marks
  geom_vline(xintercept = as.numeric(as.yearqtr("2020 Q1")), linetype=1, show.legend = T) +
  theme(axis.text.x=element_text(angle=45, hjust=1)) +
  theme(plot.title.position = 'plot', 
        plot.title = element_text(hjust = 0.5, face = "bold", size = 16)) + 
  theme(axis.text=element_text(size=16),
        axis.title=element_text(size=20,face="bold"))

ceo_i_decade <- ceo_i_decade  + annotate("text", x = 2011.5 , y = 1.6,
                     label = "paste(italic(d[2010]), \" = -0.012\")", parse = TRUE,size = 5) + 
  annotate("text", x = 2015.5 , y = 1.6,label = "paste(italic(d[2015]), \" = 0.024\")", parse = TRUE,size = 5)

We-usage

we_decade <- 
  ggplot() +
  geom_line(data = df2, aes(x = Date, y = we_mean, color = "CEO"), #set our colors 
              linetype = 'solid') +
  geom_ribbon(data = df2, aes(x = Date,ymin=we_mean-we_std.error, ymax=we_mean+we_std.error, fill = "CEO"), alpha=0.2) +
geom_line(data = reddit_tidy, aes(x = Quarter, y = we_mean, color = "Reddit"), #set our colors 
              linetype = 'solid') +
geom_ribbon(data = reddit_tidy, aes(x = Quarter,ymin=we_mean-we_std.error, ymax=we_mean+we_std.error, fill = "Reddit"), alpha=0.2) +
  ggtitle("We-usage") + 
  labs(x = "Fiscal Quarter", y = '% of Total Words') +
  plot_aes + #here's our plot aes object
  scale_x_yearqtr(name="Fiscal Quarter",format = "%YQ%q", n= 5) + #change the N to change the number of tick marks
  geom_vline(xintercept = as.numeric(as.yearqtr("2020 Q1")), linetype=1, show.legend = T) +
  theme(axis.text.x=element_text(angle=45, hjust=1)) +
  theme(plot.title.position = 'plot', 
        plot.title = element_text(hjust = 0.5, face = "bold", size = 16)) + 
  theme(axis.text=element_text(size=16),
        axis.title=element_text(size=20,face="bold"))

we <- we_decade + annotate("text", x = 2011.5 , y = 5.25,
                     label = "paste(italic(d[2010]), \" = 0.156\")", parse = TRUE,size = 5) + 
  annotate("text", x = 2015.5 , y = 5.25,label = "paste(italic(d[2015]), \" = 0.112\")", parse = TRUE,size = 5)

we <- we +
  scale_color_manual(values = c(CEO = "dodgerblue3", Reddit = "red")) +
  scale_fill_manual(values = c(CEO = "dodgerblue3", Reddit = "red"))

we

ceo_we_decade <- ggplot(data=df2, aes(x=Date, y=we_mean, group=1)) +
  geom_line(colour = "dodgerblue3") +
  #geom_point() +
  #scale_x_date(date_breaks = "1 month", date_labels = "%Y-%m") +
  ggtitle("Collective-focus") + 
  plot_aes + #here's our plot aes object
  geom_ribbon(aes(ymin=we_mean-we_std.error, ymax=we_mean+we_std.error),fill = 'dodgerblue3', alpha=0.2) +
  labs(x = "Month", y = '% of total words') +
  scale_x_yearqtr(name="Fiscal Quarter",format = "%YQ%q", n= 5) + #change the N to change the number of tick marks
  geom_vline(xintercept = as.numeric(as.yearqtr("2020 Q1")), linetype=1, show.legend = T) +
  theme(axis.text.x=element_text(angle=45, hjust=1)) +
  theme(plot.title.position = 'plot', 
        plot.title = element_text(hjust = 0.5, face = "bold", size = 16)) + 
  theme(axis.text=element_text(size=16),
        axis.title=element_text(size=20,face="bold"))
ceo_we_decade <- ceo_we_decade + annotate("text", x = 2011.5 , y = 5.25,
                     label = "paste(italic(d[2010]), \" = 0.156\")", parse = TRUE,size = 5) + 
  annotate("text", x = 2015.5 , y = 5.25,label = "paste(italic(d[2015]), \" = 0.112\")", parse = TRUE,size = 5)

Tie together

graphs <- ggpubr::ggarrange(analytic,cogproc,i,we,ncol=2, nrow=2, common.legend = TRUE, legend = "bottom")
annotate_figure(graphs,
                top = text_grob("CEOs' Language Change over the Decade compared to reddit data", color = "black", face = "bold", size = 20))

CEO only graphs

graphs <- ggpubr::ggarrange(CEO_Analytic_decade,ceo_cogproc_decade,ceo_i_decade,ceo_we_decade,ncol=2, nrow=2, common.legend = TRUE, legend = "bottom")
annotate_figure(graphs,
                top = text_grob("CEOs' Language Change over the Decade", color = "black", face = "bold", size = 20))

Package Citations

report::cite_packages()

##   - Grolemund G, Wickham H (2011). "Dates and Times Made Easy with lubridate." _Journal of Statistical Software_, *40*(3), 1-25. <https://www.jstatsoft.org/v40/i03/>.
##   - J L (2006). "Plotrix: a package in the red light district of R." _R-News_, *6*(4), 8-12.
##   - Kassambara A (2023). _ggpubr: 'ggplot2' Based Publication Ready Plots_. R package version 0.6.0, <https://CRAN.R-project.org/package=ggpubr>.
##   - Kuhn M (2022). _caret: Classification and Regression Training_. R package version 6.0-93, <https://CRAN.R-project.org/package=caret>.
##   - Lin G (2022). _reactable: Interactive Data Tables Based on 'React Table'_. R package version 0.3.0, <https://CRAN.R-project.org/package=reactable>.
##   - Müller K, Wickham H (2023). _tibble: Simple Data Frames_. R package version 3.2.1, <https://CRAN.R-project.org/package=tibble>.
##   - R Core Team (2022). _R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. <https://www.R-project.org/>.
##   - Rinker TW, Kurkiewicz D (2018). _pacman: Package Management for R_. version 0.5.0, <http://github.com/trinker/pacman>.
##   - Robinson D, Hayes A, Couch S (2023). _broom: Convert Statistical Objects into Tidy Tibbles_. R package version 1.0.4, <https://CRAN.R-project.org/package=broom>.
##   - Sarkar D (2008). _Lattice: Multivariate Data Visualization with R_. Springer, New York. ISBN 978-0-387-75968-5, <http://lmdvr.r-forge.r-project.org>.
##   - Torchiano M (2020). _effsize: Efficient Effect Size Computation_. doi:10.5281/zenodo.1480624 <https://doi.org/10.5281/zenodo.1480624>, R package version 0.8.1, <https://CRAN.R-project.org/package=effsize>.
##   - Wickham H (2016). _ggplot2: Elegant Graphics for Data Analysis_. Springer-Verlag New York. ISBN 978-3-319-24277-4, <https://ggplot2.tidyverse.org>.
##   - Wickham H (2022). _stringr: Simple, Consistent Wrappers for Common String Operations_. R package version 1.5.0, <https://CRAN.R-project.org/package=stringr>.
##   - Wickham H (2023). _forcats: Tools for Working with Categorical Variables (Factors)_. R package version 1.0.0, <https://CRAN.R-project.org/package=forcats>.
##   - Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). "Welcome to the tidyverse." _Journal of Open Source Software_, *4*(43), 1686. doi:10.21105/joss.01686 <https://doi.org/10.21105/joss.01686>.
##   - Wickham H, François R, Henry L, Müller K, Vaughan D (2023). _dplyr: A Grammar of Data Manipulation_. R package version 1.1.2, <https://CRAN.R-project.org/package=dplyr>.
##   - Wickham H, Henry L (2023). _purrr: Functional Programming Tools_. R package version 1.0.1, <https://CRAN.R-project.org/package=purrr>.
##   - Wickham H, Hester J, Bryan J (2023). _readr: Read Rectangular Text Data_. R package version 2.1.4, <https://CRAN.R-project.org/package=readr>.
##   - Wickham H, Vaughan D, Girlich M (2023). _tidyr: Tidy Messy Data_. R package version 1.3.0, <https://CRAN.R-project.org/package=tidyr>.
##   - Zeileis A, Grothendieck G (2005). "zoo: S3 Infrastructure for Regular and Irregular Time Series." _Journal of Statistical Software_, *14*(6), 1-27. doi:10.18637/jss.v014.i06 <https://doi.org/10.18637/jss.v014.i06>.
##   - Zhu H (2021). _kableExtra: Construct Complex Table with 'kable' and Pipe Syntax_. R package version 1.3.4, <https://CRAN.R-project.org/package=kableExtra>.